Statistical and Algorithmic Approaches to Bullet Matching

Heike Hofmann

Statistical and Algorithmic Approaches to Bullet Matching







Heike Hofmann, Alicia Carriquiry, Eric Hare, Haley Jeppson
Center for Statistics and Applications in Forensic Evidence
Iowa State University
June 8th, 2017

Outline

x3p format: ISO 25178-72:2017

Automated Matching Algorithm: Front-End Web Application

https://isu-csafe.stat.iastate.edu/shiny/bulletr/

Automated Matching Algorithm

Automated Matching Algorithm: Extract Features

Features are extracted from aligned signaturs \(f\) and \(g\):

More Features

Reference database

Distribution of Features

Data-driven rules

Algorithm: Forest of 1000 trees

Feature Importance

How much of a land do we need for a match?

Simulation Study for degradation

  1. Three types of Degradation:
    1. Left Fixed - The left portion of the land (leading shoulder) is recoverable.
    2. Middle Fixed - The middle portion of the land is recoverable.
    3. Right Fixed - The right portion of the land (trailing shoulder) is recoverable.
  2. Six Degradation Levels: 100% (Fully recovered), 87.5% Recovered, 75% Recovered, 62.5% Recovered, 50% Recovered, 37.5% Recovered, 25% Recovered.

Simulation Results

Testing this Finding

To come full circle, we will attempt to extract a 50% degraded signature from a Hamby bullet land with bad tank rash in one half. (Barrel 9 Bullet 2 Land 4):

Br924 Results

Extracting the ideal signature and then simulating a left-fixed 50% degradation scenario yields the following:

Testing the Model


Transparency & Reproducibility

Future work: improve matching

Future work: other

  1. investigate sources of (statistical) error: how much variability is introduced due to operator, lab, type of microscope? (ties into efforts by Martin Baiker from NFI and NIST)
  2. expand on applications: e.g. primer shearing marks:
  3. Aspect of transparency: making data and algorithms accessible to non-expert users

Thank You

Special thanks to Alan Zheng at the National Institute of Standards and Technology for maintaining the NIST Ballistics Toolmark Research Database and providing many useful suggestions.

References

Biasotti, Alfred A. 1959. “A Statistical Study of the Individual Characteristics of Fired Bullets.” Journal of Forensic Sciences 4 (1): 34–50.

Chumbley, L Scott, Max D Morris, M James Kreiser, Charles Fisher, Jeremy Craft, Lawrence J Genalo, Stephen Davis, David Faden, and Julie Kidd. 2010. “Validation of Tool Mark Comparisons Obtained Using a Quantitative, Comparative, Statistical Algorithm.” Journal of Forensic Sciences 55 (4): 953–61.

Clarkson, James A, and C Raymond Adams. 1933. “On Definitions of Bounded Variation for Functions of Two Variables.” Transactions of the American Mathematical Society 35 (4). JSTOR: 824–54.

Hamby, James E., David J. Brundage, and James W. Thorpe. 2009. “The Identification of Bullets Fired from 10 Consecutively Rifled 9mm Ruger Pistol Barrels: A Research Project Involving 507 Participants from 20 Countries.” AFTE Journal 41 (2): 99–110.

Vorburger, T.V., J.-F. Song, W. Chu, L. Ma, S.H. Bui, A. Zheng, and T.B. Renegar. 2011. “Applications of Cross-Correlation Functions.” Wear 271 (3–4): 529–33. doi:http://dx.doi.org/10.1016/j.wear.2010.03.030.